Auditory startle disrupts speech coordination

Speech production requires temporal coordination between the actions of different functional groupings of muscles in the human body. Crucially, such functionally organized units, or “modules”, may be susceptible to disruption by an external stimulus such as a startling auditory stimulus (SAS; >120dB), enabling a possible window into the internal structure of learned speech movements. Following on the observation that SAS is known to accelerate the release of pre-planned actions, the current study examines lip kinematics in SAS-induced responses during speech movements to test whether this accelerated release applies on the scale of entire syllables or on the scale of smaller functional units. Production measures show that SAS-elicited bilabial movements in [ba] syllables are prone to disruption as measured by discontinuity in velocity profiles. We use a 3D finite element method (FEM) biomechanical model to simulate the temporal interaction between muscle groupings in speech. Simulation results indicate that this discontinuity can be accounted for as an instance of temporally decoupled coordination across neuromuscular modules. In such instances, the muscle groupings controlling lip compression and jaw opening, which normally fire sequentially, appear more likely to be activated synchronously.

Spatio-Temporal Analysis of Hydropower Projects with Terrestrial Environmentally Sensitive Areas of Nepal

The hydropower project’s construction is increasing that can affect the terrestrial environment. Hydropower projects located in environmentally sensitive areas have higher environmental impacts, so I analyzed the spatio-temporal interaction between hydropower projects’ locations and terrestrial environmentally sensitive areas of Nepal to visualize the probable environmental impacts. I found that most of the existing projects lie on the hill, however, future projects are moving northward. Among the 12 eco-regions of Nepal, hydropower projects are located in 10 eco-regions. Hydropower projects were found to interact with more than half of biodiverse areas of the country (28 out of 45), and more than five thousand megawatts of hydropower projects are located completely inside these biodiverse areas. The study suggests that the interaction between hydropower projects and environmentally sensitive areas might increase in the future. Hydropower projects should avoid environmentally sensitive areas such as biodiverse areas and protected areas as far as possible to minimize the impacts. Rapid hydropower development is a necessity in countries like Nepal, so further studies on the impacts of hydropower projects on environmentally sensitive areas as well as improvement of the quality of the environmental assessment of the projects are necessary for environment-friendly development.

Real-time analysis and forecasting of the microseismic cloud size: Physics-based models versus machine learning

The spatiotemporal distribution of hydraulic fracturing microseismicity is complicated and depends on various mechanical and diffusional parameters. Hydraulic fracture modeling can aid in understanding fracture propagation and microseismicity. Nevertheless, the complex spatial and temporal interaction of several processes occurring within and around the fracture represents a challenge for developing real-time tools for microseismic prediction. Two approaches were developed to forecast the microseismic cloud size in real-time. The first approach uses fracture propagation models to derive the cloud size directly from the microseismic observations. The second approach is based on a convolutional neural network (CNN) trained with the engineering parameters and past microseismic cloud size values. A rolling-forecasting strategy is employed to train consecutive CNN models in real-time to make predictions at a specified time lag. A data augmentation technique known as double noise injection is used to ensure that the amount of training examples available to the machine learning models at each time step is similar or larger than the number of free parameters. Results show that the CNN outperforms the quality of predictions of the physics-based models but with a reduced prediction capability. The physics-based approach can predict growth at any time but ignores the engineering parameters. In addition, the physics-based methods lead to real-time insights into the fracturing regime, revealing whether microseismicity is most likely generated due to a leak-off-dominated or a storage-dominated regime. The CNN model can forecast the cloud size only at a single future time lag while using the engineering parameters and past cloud growth as input. However, this approach does not provide a physical interpretation of the fracture propagation regime. The prediction accuracy of both methodologies varies depending on the microseismic behavior. We postulate that the CNN forecasts could be improved by including more physical constraints into the predictive model.

Investigating scientific mobility in co-authorship networks using multilayer temporal motifs

This paper introduces a framework for understanding complex temporal interaction patterns in large-scale scientific collaboration networks. In particular, we investigate how two key concepts in science studies, scientific collaboration and scientific mobility, are related and possibly differ between fields. We do so by analyzing multilayer temporal motifs: small recurring configurations of nodes and edges.Driven by the problem that many papers share the same publication year, we first provide a methodological contribution: an efficient counting algorithm for multilayer temporal motifs with concurrent edges. Next, we introduce a systematic categorization of the multilayer temporal motifs, such that each category reflects a pattern of behavior relevant to scientific collaboration and mobility. Here, a key question concerns the causal direction: does mobility lead to collaboration or vice versa? Applying this framework to scientific collaboration networks extracted from Web of Science (WoS) consisting of up to 7.7 million nodes (authors) and 94 million edges (collaborations), we find that international collaboration and international mobility reciprocally influence one another. Additionally, we find that Social sciences & Humanities (SSH) scholars co-author to a greater extent with authors at a distance, while Mathematics & Computer science (M&C) scholars tend to continue to collaborate within the established knowledge network and organization.

Bayesian Spatio-temporal Analysis of Greenhouse Gas Emissions in Africa

Global warming is a driver of climate change and is attributed to the increasing concentration of greenhouse gases in the atmosphere due to human activities. Although Africa contributes the least to global greenhouse gas emissions, its emissions are still on the increase. This study analyzes the spatial effect, temporal effect, and the interaction of these effects on these emissions in Africa. A 27-year greenhouse gas emissions data of some selected African countries was studied using Bayesian Spatio-temporal analysis within a Bayesian framework. Inference was based on integrated nested Laplace approximation implemented using the R-INLA package in R. Various subsets of Spatio-temporal models were fitted, including those that accounted for boundary shared among countries. Results show that models with the Spatio-temporal interaction effect outperform models that did not take this effect into account, confirming findings from existing literature. Findings from this study also revealed that the boundary shared among countries impacts greenhouse gas emissions. Countries that are less likely to have high greenhouse gas emissions but shared boundaries with those likely to have high estimates eventually had a high estimate of emissions over a long period. Controlling and reducing greenhouse gas emissions in Africa should be a collective effort, particularly among countries sharing boundaries.